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CC carrier-relationship-management

用于管理承运商组合、协商运费、跟踪承运商绩效、分配货运以及维护战略承运商关系的编码专业知识。基于拥有15年以上经验的运输经理提供的信息。包括记分卡框架、RFP流程、市场情报和合规性审查。适用于管理承运商、协商费率、评估承运商绩效或制定货运策略时使用。

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 1 file body ≈ 2 196 tokens Open the sourcegithub.com↗ analyzed 26 h ago

用于管理承运商组合、协商运费、跟踪承运商绩效、分配货运以及维护战略承运商关系的编码专业知识。基于拥有15年以上经验的运输经理提供的信息。包括记分卡框架、RFP流程、市场情报和合规性审查。适用于管理承运商、协商费率、评估承运商绩效或制定货运策略时使用。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureProcurementtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ECC

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "origin"

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 69 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2196 tokens
  • 100Running it twice. No mutating operations
  • low 11 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 125: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 69 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.